Prepare for v2 development: Add MCP integration and future development planning
- Add FUTURE_DEVELOPMENT.md with comprehensive v2 protocol specification - Add MCP integration design and implementation foundation - Add infrastructure and deployment configurations - Update system architecture for v2 evolution 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
anthonyrawlins
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examples/collaborative-review-example.py
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examples/collaborative-review-example.py
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#!/usr/bin/env python3
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"""
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BZZZ MCP Integration Example: Collaborative Code Review
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======================================================
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This example demonstrates how GPT-4 agents collaborate through the BZZZ MCP
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integration to perform a comprehensive code review.
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Scenario: A pull request requires review from multiple specialized agents:
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- Architect Agent: Reviews system design and architecture implications
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- Security Agent: Analyzes security vulnerabilities
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- Performance Agent: Evaluates performance impact
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- Documentation Agent: Ensures proper documentation
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The agents coordinate through BZZZ semantic addressing and threaded conversations.
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"""
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import asyncio
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import json
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import os
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import sys
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from typing import Dict, List, Any, Optional
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from dataclasses import dataclass
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from mcp import ClientSession, StdioServerParameters
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from mcp.client.stdio import stdio_client
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# Add the parent directory to the path to import BZZZ modules
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sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
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@dataclass
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class CodeReviewTask:
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"""Represents a code review task"""
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repository: str
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pull_request_number: int
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title: str
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description: str
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files_changed: List[str]
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lines_of_code: int
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complexity_score: float
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security_risk: str # low, medium, high
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@dataclass
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class AgentRole:
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"""Defines an agent role and its responsibilities"""
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name: str
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specialization: str
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capabilities: List[str]
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system_prompt: str
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class CollaborativeReviewOrchestrator:
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"""Orchestrates collaborative code review using BZZZ MCP integration"""
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def __init__(self):
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self.mcp_session: Optional[ClientSession] = None
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self.agents: Dict[str, AgentRole] = {}
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self.active_threads: Dict[str, Dict] = {}
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async def initialize(self):
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"""Initialize MCP connection to BZZZ server"""
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# Connect to the BZZZ MCP server
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server_params = StdioServerParameters(
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command="node",
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args=["/home/tony/chorus/project-queues/active/BZZZ/mcp-server/dist/index.js"]
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)
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self.mcp_session = await stdio_client(server_params)
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print("✅ Connected to BZZZ MCP Server")
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# Define agent roles
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self.define_agent_roles()
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def define_agent_roles(self):
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"""Define the specialized agent roles for code review"""
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self.agents = {
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"architect": AgentRole(
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name="architect",
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specialization="system_architecture",
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capabilities=["system_design", "architecture_review", "scalability_analysis"],
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system_prompt="""You are a senior software architect reviewing code changes.
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Focus on: architectural consistency, design patterns, system boundaries,
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scalability implications, and integration concerns."""
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),
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"security": AgentRole(
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name="security_expert",
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specialization="security_analysis",
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capabilities=["security_review", "vulnerability_analysis", "threat_modeling"],
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system_prompt="""You are a security expert reviewing code for vulnerabilities.
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Focus on: input validation, authentication, authorization, data protection,
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injection attacks, and secure coding practices."""
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),
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"performance": AgentRole(
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name="performance_expert",
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specialization="performance_optimization",
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capabilities=["performance_analysis", "optimization", "profiling"],
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system_prompt="""You are a performance expert reviewing code efficiency.
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Focus on: algorithmic complexity, memory usage, database queries,
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caching strategies, and performance bottlenecks."""
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),
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"documentation": AgentRole(
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name="documentation_specialist",
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specialization="technical_writing",
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capabilities=["documentation_review", "api_documentation", "code_comments"],
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system_prompt="""You are a documentation specialist ensuring code clarity.
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Focus on: code comments, API documentation, README updates,
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inline documentation, and knowledge transfer."""
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)
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}
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async def start_collaborative_review(self, task: CodeReviewTask) -> Dict[str, Any]:
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"""Start a collaborative review process for the given task"""
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print(f"🔍 Starting collaborative review for PR #{task.pull_request_number}")
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# Step 1: Announce agents to BZZZ network
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await self.announce_agents()
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# Step 2: Create semantic addresses for the review
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review_address = f"bzzz://*:*@{task.repository}:pr{task.pull_request_number}/review"
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# Step 3: Determine required agent roles based on task characteristics
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required_roles = self.determine_required_roles(task)
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print(f"📋 Required roles: {', '.join(required_roles)}")
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# Step 4: Create collaborative thread
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thread_id = await self.create_review_thread(task, required_roles)
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print(f"💬 Created review thread: {thread_id}")
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# Step 5: Coordinate the review process
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review_results = await self.coordinate_review(thread_id, task, required_roles)
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# Step 6: Generate final review summary
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final_summary = await self.generate_review_summary(thread_id, review_results)
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print("✅ Collaborative review completed")
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return final_summary
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async def announce_agents(self):
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"""Announce all agent roles to the BZZZ network"""
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if not self.mcp_session:
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raise RuntimeError("MCP session not initialized")
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for role_name, role in self.agents.items():
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result = await self.mcp_session.call_tool(
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"bzzz_announce",
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{
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"agent_id": f"review_agent_{role_name}",
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"role": role.name,
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"capabilities": role.capabilities,
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"specialization": role.specialization,
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"max_tasks": 2
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}
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)
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print(f"📡 Announced {role_name} agent: {result.content[0].text}")
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def determine_required_roles(self, task: CodeReviewTask) -> List[str]:
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"""Determine which agent roles are needed based on task characteristics"""
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required = ["architect"] # Architect always participates
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# Add security expert for medium/high risk changes
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if task.security_risk in ["medium", "high"]:
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required.append("security")
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# Add performance expert for large/complex changes
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if task.lines_of_code > 500 or task.complexity_score > 7.0:
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required.append("performance")
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# Add documentation expert if documentation files changed
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doc_files = [f for f in task.files_changed if f.endswith(('.md', '.rst', '.txt'))]
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if doc_files or task.lines_of_code > 200:
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required.append("documentation")
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return required
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async def create_review_thread(self, task: CodeReviewTask, required_roles: List[str]) -> str:
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"""Create a threaded conversation for the review"""
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if not self.mcp_session:
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raise RuntimeError("MCP session not initialized")
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participants = [f"review_agent_{role}" for role in required_roles]
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result = await self.mcp_session.call_tool(
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"bzzz_thread",
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{
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"action": "create",
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"topic": f"Code Review: {task.title}",
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"participants": participants
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}
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)
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response_data = json.loads(result.content[0].text)
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return response_data["result"]["thread_id"]
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async def coordinate_review(self, thread_id: str, task: CodeReviewTask, required_roles: List[str]) -> Dict[str, Any]:
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"""Coordinate the collaborative review process"""
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review_results = {}
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# Step 1: Share task context with all agents
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await self.share_task_context(thread_id, task)
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# Step 2: Each agent performs their specialized review
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for role in required_roles:
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print(f"🔍 {role} agent performing review...")
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agent_review = await self.conduct_role_specific_review(thread_id, role, task)
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review_results[role] = agent_review
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# Step 3: Facilitate cross-agent discussion
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discussion_results = await self.facilitate_discussion(thread_id, review_results)
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review_results["discussion"] = discussion_results
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# Step 4: Reach consensus on final recommendations
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consensus = await self.reach_consensus(thread_id, review_results)
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review_results["consensus"] = consensus
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return review_results
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async def share_task_context(self, thread_id: str, task: CodeReviewTask):
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"""Share the task context with all thread participants"""
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if not self.mcp_session:
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raise RuntimeError("MCP session not initialized")
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context_message = {
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"task": {
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"repository": task.repository,
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"pr_number": task.pull_request_number,
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"title": task.title,
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"description": task.description,
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"files_changed": task.files_changed,
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"lines_of_code": task.lines_of_code,
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"complexity_score": task.complexity_score,
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"security_risk": task.security_risk
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},
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"review_guidelines": {
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"focus_areas": ["correctness", "security", "performance", "maintainability"],
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"severity_levels": ["critical", "major", "minor", "suggestion"],
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"collaboration_expected": True
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}
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}
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target_address = f"bzzz://*:*@{task.repository}:pr{task.pull_request_number}/context"
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await self.mcp_session.call_tool(
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"bzzz_post",
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{
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"target_address": target_address,
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"message_type": "task_context",
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"content": context_message,
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"thread_id": thread_id,
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"priority": "high"
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}
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)
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async def conduct_role_specific_review(self, thread_id: str, role: str, task: CodeReviewTask) -> Dict[str, Any]:
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"""Simulate a role-specific review (in real implementation, this would call GPT-4)"""
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print(f" Analyzing {len(task.files_changed)} files for {role} concerns...")
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# Simulate different review outcomes based on role
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review_data = {
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"architect": {
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"findings": [
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"Code follows established patterns",
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"Consider extracting common functionality into utility class",
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"Database schema changes require migration script"
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],
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"severity": "minor",
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"recommendations": ["Refactor common code", "Add migration script"],
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"approval_status": "approved_with_suggestions"
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},
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"security": {
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"findings": [
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"Input validation implemented correctly",
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"SQL injection protection in place",
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"Consider adding rate limiting for API endpoints"
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],
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"severity": "minor",
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"recommendations": ["Add rate limiting", "Update security documentation"],
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"approval_status": "approved_with_suggestions"
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},
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"performance": {
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"findings": [
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"Database queries are optimized",
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"Memory usage looks reasonable",
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"Consider caching for frequently accessed data"
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],
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"severity": "suggestion",
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"recommendations": ["Implement caching strategy", "Add performance monitoring"],
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"approval_status": "approved"
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},
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"documentation": {
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"findings": [
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"API documentation updated",
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"Some complex functions lack comments",
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"README needs update for new features"
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],
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"severity": "minor",
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"recommendations": ["Add function comments", "Update README"],
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"approval_status": "approved_with_suggestions"
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}
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}.get(role, {})
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# Post review findings to the thread
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await self.post_review_findings(thread_id, role, review_data, task)
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return review_data
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async def post_review_findings(self, thread_id: str, role: str, review_data: Dict, task: CodeReviewTask):
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"""Post review findings to the collaborative thread"""
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if not self.mcp_session:
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raise RuntimeError("MCP session not initialized")
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message_content = {
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"reviewer": role,
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"review_type": "initial_review",
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"findings": review_data.get("findings", []),
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"severity": review_data.get("severity", "info"),
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"recommendations": review_data.get("recommendations", []),
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"approval_status": review_data.get("approval_status", "pending"),
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"timestamp": "2025-01-07T12:00:00Z"
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}
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target_address = f"bzzz://*:{role}@{task.repository}:pr{task.pull_request_number}/findings"
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await self.mcp_session.call_tool(
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"bzzz_post",
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{
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"target_address": target_address,
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"message_type": "review_findings",
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"content": message_content,
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"thread_id": thread_id,
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"priority": "medium"
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}
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)
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async def facilitate_discussion(self, thread_id: str, review_results: Dict[str, Any]) -> Dict[str, Any]:
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"""Facilitate cross-agent discussion about conflicting or overlapping concerns"""
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print("💭 Facilitating inter-agent discussion...")
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# Identify areas where multiple agents have concerns
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common_concerns = self.identify_common_concerns(review_results)
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discussion_points = []
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for concern in common_concerns:
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discussion_point = {
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"topic": concern["area"],
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"agents_involved": concern["agents"],
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"severity_levels": concern["severities"],
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"proposed_resolution": concern["suggested_approach"]
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}
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discussion_points.append(discussion_point)
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# Simulate discussion outcomes
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discussion_results = {
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"discussion_points": discussion_points,
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"resolved_conflicts": len(discussion_points),
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"consensus_reached": True,
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"escalation_needed": False
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}
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return discussion_results
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def identify_common_concerns(self, review_results: Dict[str, Any]) -> List[Dict]:
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"""Identify areas where multiple agents have overlapping concerns"""
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# This would analyze the review findings to find common themes
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# For demo purposes, return a sample concern
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return [
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{
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"area": "error_handling",
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"agents": ["architect", "security"],
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"severities": ["minor", "minor"],
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"suggested_approach": "Implement consistent error handling pattern"
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}
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]
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async def reach_consensus(self, thread_id: str, review_results: Dict[str, Any]) -> Dict[str, Any]:
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"""Facilitate consensus-building among reviewing agents"""
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print("🤝 Building consensus on final recommendations...")
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# Aggregate all findings and recommendations
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all_findings = []
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all_recommendations = []
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approval_statuses = []
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for role, results in review_results.items():
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if role == "discussion":
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continue
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all_findings.extend(results.get("findings", []))
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all_recommendations.extend(results.get("recommendations", []))
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approval_statuses.append(results.get("approval_status", "pending"))
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# Determine overall approval status
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if all(status == "approved" for status in approval_statuses):
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overall_status = "approved"
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elif any(status == "rejected" for status in approval_statuses):
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overall_status = "rejected"
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else:
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overall_status = "approved_with_changes"
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consensus = {
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"overall_approval": overall_status,
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"critical_issues": 0,
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"major_issues": 1,
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"minor_issues": 4,
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"suggestions": 3,
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"consolidated_recommendations": list(set(all_recommendations)),
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"requires_changes": overall_status != "approved",
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"consensus_confidence": 0.95
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}
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return consensus
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async def generate_review_summary(self, thread_id: str, review_results: Dict[str, Any]) -> Dict[str, Any]:
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"""Generate a comprehensive review summary"""
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if not self.mcp_session:
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raise RuntimeError("MCP session not initialized")
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# Use thread summarization tool
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summary_result = await self.mcp_session.call_tool(
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"bzzz_thread",
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{
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"action": "summarize",
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"thread_id": thread_id
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}
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)
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thread_summary = json.loads(summary_result.content[0].text)
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final_summary = {
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"review_id": f"review_{thread_id}",
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"overall_status": review_results.get("consensus", {}).get("overall_approval", "pending"),
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"participating_agents": list(self.agents.keys()),
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"thread_summary": thread_summary,
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"key_findings": self.extract_key_findings(review_results),
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"action_items": self.generate_action_items(review_results),
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"approval_required": review_results.get("consensus", {}).get("requires_changes", True),
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"estimated_fix_time": "2-4 hours",
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"review_completed_at": "2025-01-07T12:30:00Z"
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}
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return final_summary
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def extract_key_findings(self, review_results: Dict[str, Any]) -> List[str]:
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"""Extract the most important findings from all agent reviews"""
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key_findings = []
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for role, results in review_results.items():
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if role in ["discussion", "consensus"]:
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continue
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findings = results.get("findings", [])
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# Take first 2 findings from each agent as key findings
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key_findings.extend(findings[:2])
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return key_findings
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def generate_action_items(self, review_results: Dict[str, Any]) -> List[Dict]:
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"""Generate actionable items based on review findings"""
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action_items = []
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consensus = review_results.get("consensus", {})
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for rec in consensus.get("consolidated_recommendations", []):
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action_items.append({
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"action": rec,
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"priority": "medium",
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"estimated_effort": "1-2 hours",
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||||
"assignee": "developer"
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||||
})
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||||
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||||
return action_items
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||||
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||||
async def cleanup(self):
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||||
"""Clean up resources and close connections"""
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||||
if self.mcp_session:
|
||||
await self.mcp_session.close()
|
||||
print("🧹 Cleaned up MCP session")
|
||||
|
||||
|
||||
async def main():
|
||||
"""Main example demonstrating collaborative code review"""
|
||||
|
||||
# Sample code review task
|
||||
task = CodeReviewTask(
|
||||
repository="bzzz-system",
|
||||
pull_request_number=123,
|
||||
title="Add user authentication service",
|
||||
description="Implements JWT-based authentication with role-based access control",
|
||||
files_changed=[
|
||||
"src/auth/service.py",
|
||||
"src/auth/middleware.py",
|
||||
"src/models/user.py",
|
||||
"tests/test_auth.py",
|
||||
"docs/api/auth.md"
|
||||
],
|
||||
lines_of_code=450,
|
||||
complexity_score=6.5,
|
||||
security_risk="medium"
|
||||
)
|
||||
|
||||
# Initialize the orchestrator
|
||||
orchestrator = CollaborativeReviewOrchestrator()
|
||||
|
||||
try:
|
||||
print("🚀 Initializing BZZZ MCP Collaborative Review Example")
|
||||
await orchestrator.initialize()
|
||||
|
||||
# Start the collaborative review process
|
||||
results = await orchestrator.start_collaborative_review(task)
|
||||
|
||||
# Display results
|
||||
print("\n" + "="*60)
|
||||
print("📊 COLLABORATIVE REVIEW RESULTS")
|
||||
print("="*60)
|
||||
print(json.dumps(results, indent=2))
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ Error during collaborative review: {e}")
|
||||
|
||||
finally:
|
||||
await orchestrator.cleanup()
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Run the example
|
||||
asyncio.run(main())
|
||||
Reference in New Issue
Block a user